Dean's Seminar Series: Just Machine Learning

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Professor Geoff Webb, Director of the Centre for Data Science at Monash University, on behalf of Professor Jon Whittle, Dean of the Faculty of IT, is delighted to invite you to attend the Monash Dean’s Seminar Series - Just Machine Learning.

In his 1997 Machine Learning textbook, American computer scientist Tom Mitchell defined the well-posed learning problem as follows:

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”

Associate Professor of Computer Science at Northeastern University in Boston, MA, Tina Eliassi-Rad, will expand on Tom Mitchell’s operational definition of Machine Learning while also delving into:

  • Current tasks, experiences and performance measures as they pertain to the fairness of machine learning.

  • New findings in terms of impossibility results.

  • An alternative task definition whose goal is to provide more context to the human decision-maker.

  • A null model for fairness and a demonstration of how to use deviations from this null model to measure and identify prejudices in the data.

During the event, you will not only hear from a world-leading expert in Computer Science but you will also have the opportunity to network with Monash researchers, academics and alumni.


Thursday 29 August


12.00 pm - 1.30 pm


Clayton Campus: S9, 16 Rainforest Walk

Caulfield Campus: E224, Building E (Video Conferencing)


This is a free event but seats are limited and registration is required to secure your seat

About Professor Tina Eliassi-Rad

Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also a core faculty member at Northeastern University's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory.

Tina earned her PhD in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in Data Mining and Machine Learning and spans theory, algorithms and applications of big data from networked representations of physical and social phenomena. She has over 80 peer-reviewed publications (including a few best paper and best paper runner-up awardees) and has given over 190 invited talks and 13 tutorials. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in Machine learning.

Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, she served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2010, she received an Outstanding Mentor Award from the Office of Science at the US Department of Energy.

For more details on Professor Tina Eliassi-Rad, visit

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